Optimised configuration of sensing elements for control and fault tolerance applied to an electro-magnetic suspension system

New technological advances and the requirements to increasingly abide by new safety laws in engineering design projects highly affects industrial products in areas such as automotive, aerospace and railway industries. The necessity arises to design reduced-cost hi-tech products with minimal complexity, optimal performance, effective parameter robustness properties, and high reliability with fault tolerance. In this context the control system design plays an important role and the impact is crucial relative to the level of cost efficiency of a product. Measurement of required information for the operation of the design control system in any product is a vital issue, and in such cases a number of sensors can be available to select from in order to achieve the desired system properties. However, for a complex engineering system a manual procedure to select the best sensor set subject to the desired system properties can be very complicated, time consuming or even impossible to achieve. This is more evident in the case of large number of sensors and the requirement to comply with optimum performance. The thesis describes a comprehensive study of sensor selection for control and fault tolerance with the particular application of an ElectroMagnetic Levitation system (being an unstable, nonlinear, safety-critical system with non-trivial control performance requirements). The particular aim of the presented work is to identify effective sensor selection frameworks subject to given system properties for controlling (with a level of fault tolerance) the MagLev suspension system. A particular objective of the work is to identify the minimum possible sensors that can be used to cover multiple sensor faults, while maintaining optimum performance with the remaining sensors. The tools employed combine modern control strategies and multiobjective constraint optimisation (for tuning purposes) methods. An important part of the work is the design and construction of a 25kg MagLev suspension to be used for experimental verification of the proposed sensor selection frameworks

Optimised sensor configurations for a Maglev suspension

This paper discusses a systematic approach for selecting the minimum number of sensors for an Electromagnetic levitation system that satisfies both deterministic and stochastic performance objectives. The controller tuning is based upon the utilisation of a recently developed genetic algorithm, namely NSGAII. Two controller structures are discussed, an inner loop classical solution for illustrating the efficacy of the NSGAII tuning and a Linear quadratic gaussian structure particularly on sensor optimization

Optimised sensor configurations for a MAGLEV suspension system

This paper discusses a systematic approach for selecting the minimum number of sensors for an Electromagnetic suspension system that satisfies both optimised deterministic and stochastic performance objectives. The performance is optimised by tuning the controller using evolutionary algorithms. Two controller strategies are discussed, an inner loop classical solution for illustrating the efficacy of the evolutionary algorithm and a Linear Quadratic Gaussian (LQG) structure particularly on sensor optimisation

Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system

For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system

MAGLEV suspensions - a sensor optimisation framework

In this paper, a systematic framework for optimised sensor configurations is implemented via H∞ Loop Shaping Procedure. The optimisation framework, gives the sensor sets that satisfy predefined user criteria and the preset constraints required for the MAGnetic LEVitated suspension performance via evolutionary algorithms. The scheme is assessed via appropriate simulations for its efficacy

Fault Tolerant Control for EMS systems with sensor failure

The paper presents a method to recover the performance of an EMS (Electromagnetic suspension) under faulty air gap measurement. The controller is a combination of classical control loops, a Kalman estimator and analytical redundancy (for the air gap signal). In case of a faulty air gap sensor the air gap signal is recovered using the Kalman filter and analytical redundancy. Simulations verify the proposed sensor Fault Tolerant Control (FTC) method for the EMS system

Sensor optimisation via H∞ applied to a MAGLEV suspension system

In this paper a systematic method via H∞ control design is proposed to select a sensor set that satisfies a number of input criteria for a MAGLEV suspension system. The proposed method recovers a number of optimised controllers for each possible sensor set that satisfies the performance and constraint criteria using evolutionary algorithms

Sensor optimisation via H∞ applied to a MAGLEV suspension system

In this paper a systematic method via H∞ control design is proposed to select a sensor set that satisfies a number of input criteria for a MAGLEV suspension system. The proposed method recovers a number of optimised controllers for each possible sensor set that satisfies the performance and constraint criteria using evolutionary algorithms

Optimal passive fault tolerant control of a high redundancy actuator

The High Redundancy Actuator (HRA) project deals with the construction of an actuator using many redundant actuation elements. If one element fails, this changes the behaviour slightly, but the system still remains operation. A key challenge in this project is to design a passive fault tolerant controller that maintains the required performance in the presence of faults. This paper shows how to achieve this with structurally simple controllers, by optimising the parameters using a genetic algorithm

Disturbance observer based control for nonlinear MAGLEV suspension system

This paper investigates the disturbance rejection problem of nonlinear MAGnetic LEViation (MAGLEV) suspension system with “mismatching” disturbances. Here “mismatching” refers to the disturbances that enter the system via different channel to the control input. The disturbance referring in this paper is mainly on load variation and unmodeled nonlinear dynamics. By linearizing the nonlinear MAGLEV suspension model, a linear state-space disturbance observer (DOB) is designed to estimate the lumped “mismatching” disturbances. A new disturbance compensation control method based on the estimate of DOB is proposed to solve the disturbance attenuation problem. The efficacy of the proposed approach for rejecting given disturbance is illustrated via simulations on realistic track input